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Theories of Gender in Natural Language Processing
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University, Faculty of Social Sciences, Umeå Centre for Gender Studies (UCGS). (Foundations of Language Processing)ORCID iD: 0000-0003-0278-9757
Umeå University, Faculty of Science and Technology, Department of Computing Science. Uppsala University, Sweden.ORCID iD: 0000-0002-4954-4397
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)ORCID iD: 0000-0002-4696-9787
2022 (English)In: Proceedings of the fifth annual ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT'22), 2022Conference paper, Published paper (Refereed)
Abstract [en]

The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date there is no comprehensive analysis of how “gender” is theorized in the field. We survey nearly 200 articles concerning gender bias in NLP to discover how the field conceptualizes gender both explicitly (e.g. through definitions of terms) and implicitly (e.g. through how gender is operationalized in practice). In order to get a better idea of emerging trajectories of thought, we split these articles into two sections by time.

We find that the majority of the articles do not make their theo- rization of gender explicit, even if they clearly define “bias.” Almost none use a model of gender that is intersectional or inclusive of non- binary genders; and many conflate sex characteristics, social gender, and linguistic gender in ways that disregard the existence and expe- rience of trans, nonbinary, and intersex people. There is an increase between the two time-sections in statements acknowledging that gender is a complicated reality, however, very few articles manage to put this acknowledgment into practice. In addition to analyzing these findings, we provide specific recommendations to facilitate interdisciplinary work, and to incorporate theory and methodol- ogy from Gender Studies. Our hope is that this will produce more inclusive gender bias research in NLP.

Place, publisher, year, edition, pages
2022.
Keywords [en]
natural language processing, gender bias, gender studies
National Category
Language Technology (Computational Linguistics) Gender Studies
Research subject
Computer Science; gender studies
Identifiers
URN: urn:nbn:se:umu:diva-194742DOI: 10.1145/3531146.3534627Scopus ID: 2-s2.0-85133018925OAI: oai:DiVA.org:umu-194742DiVA, id: diva2:1658474
Conference
ACM FAccT Conference 2022, Conference on Fairness, Accountability, and Transparency, Hybrid via Seoul, Soth Korea, June 21-14, 2022
Note

Alternative title: "Theories of 'Gender' in NLP Bias Research"

Available from: 2022-05-16 Created: 2022-05-16 Last updated: 2023-03-24

Open Access in DiVA

No full text in DiVA

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Publisher's full textScopusarXiv (preprint)ACM FAccT Conference 2022ACM FAccT 2022 Accepted papers

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Devinney, HannahBjörklund, JennyBjörklund, Henrik

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Devinney, HannahBjörklund, JennyBjörklund, Henrik
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